Rituximab for the treatment of connective tissue disease-associated interstitial lung disease.
Bibliographic record
Abstract
OBJECTIVE: To describe our experience with rituximab (RTX) as treatment for a diverse spectrum of chronic connective tissue disease-associated interstitial lung disease (CTD-ILD). METHODS: Twenty-four subjects with CTD-ILD were included. All had pulmonary function testing before and after their first RTX infusion. Each subject was evaluated in a multidisciplinary autoimmune and ILD outpatient clinic. Data were extracted by retrospective review of complete medical records. RESULTS: Most subjects were middle-aged white women with rheumatoid arthritis (RA) (n=15) and a nonspecific interstitial pneumonia (NSIP) pattern on high-resolution chest computed tomography scans (n=17). Sixteen subjects received a corticosteroid-sparing agent at the time of RTX initiation; mostly mycophenolate mofetil (n=8). RTX administration was not associated with corticosteroid-sparing effects: 13 subjects were on prednisone at the time of the initial RTX cycle, and 9 remained on prednisone at 6 months after (mean daily dosage 10.2±16.2 mg before vs. 5.6±11.0 mg after, p=0.27). RTX had no appreciable effect on pulmonary physiology; however, individual trajectories for percentage predicted forced vital capacity (FVC%) were highly variable. The underlying CTD (RA vs. non-RA) and ILD pattern did not appear to affect response to RTX. Among 14 subjects who received multiple RTX cycles, FVC% trajectories were variable: FVC% increased in eight and declined in six. Respiratory infections were the most common post-RTX adverse event. CONCLUSION: In this small, retrospective study of chronic CTD-ILD, RTX was not associated with changes in FVC% or corticosteroid-sparing effects. Controlled, prospective studies are needed to more confidently define the effects of RTX in CTD-ILD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".